Adaptive management is an explicit and analytical process for adjusting
management and research decisions to better achieve management objectives;
and this process should be quantitative wherever feasible. Adaptive management
recognizes that knowledge about natural resource systems is uncertain.
Therefore, some management actions are best conducted as experiments in
a continuing attempt to reduce the risk arising from that uncertainty.
The aim of such experimentation is to find a way to achieve the objectives
as quickly as possible while avoiding inadvertent mistakes that could lead
to unsatisfactory results.

The concept of adaptive management is readily understood because it represents
the common sense of "learning by doing". However, actually implementing
adaptive management is neither simple nor intuitive. This complexity stems
from the large number of interconnected potential scenarios, the related
uncertainties, and the intricacy of necessary computations. Advanced technologies
provide the decision support tools to help managers organize the relevant
information, simplify the analysis of the scenarios, and assist in the
search for optimal solutions.

Why the Current Attention On Decision Support
Systems?

Adaptive management and decision support systems are gathering increased
attention in natural resource management because three important trends
seem to be changing the way managers and biologists must address resource
issues. First and foremost, the stakes have gone up. Natural resource decisions
increasingly are at the center of intense economic, political, legal, and
value conflicts, as evidenced in the land management of many National Wildlife
Refuges, National Forests, National Parks, and other public lands. Clearly,
work with high profile endangered species and determinations of candidate
species are other examples where the stakes are increasingly high, but
many other less visible management actions are no less important nor complex.
Second, the availability of certain kinds of data has exploded, and managers
want to make the best use of that data and associated knowledge. Remote
sensing, geographic positioning systems, and various monitoring technologies
(datapods, coded wire tags, PIT tags, satellite telemetry) can generate
previously unimaginable volumes of data, but often these data are somewhat
indirect as indicators of the quantities and qualities that actually need
to be known for management purposes. The third trend is that computer modeling
is playing an increasing but sometimes controversial role. The complexity
of the systems that need to be understood in an attempt to strike the correct
balance in management decisions often necessitates computer modeling. However,
the common phenomenon of "dueling models," where different constituencies
present models that predict very different outcomes, has raised legitimate
concerns about the reliability of models.

Rigorous decision analysis and scientific support can address all three
trends by providing resource managers a set of tools to: (1.) quantify
costs and benefits, and evaluate trade-offs; (2.) assimilate all the available
data into the decision process; and (3.) assess the level of certainty
in predictions, and take this into account in making decisions. Furthermore,
the collaborative nature encourages broader buy-in by a variety of managers
with different perspectives as well as involving research scientists. This
facilitates a more participatory and cooperative atmosphere, in addition
to helping managers arrive at better decisions.

The Reality of Tough Decisions

In many resource management situations, it is not uncommon for a decision
making conflict to exist. On the one hand, we might be willing to take
a chance that our knowledge about the system is "good enough".
On this basis, we take an educated guess about the best choice for immediate
and substantial progress towards reaching objectives. If the system is
fairly static and we know a lot about how it functions, clear management
choices may indeed exist. On the other hand, we often find ourselves working
in situations where predicting future conditions is difficult at best,
or where information about the system is not well documented. A flexible
process of engaging in management experiments could be followed to increase
our knowledge about the system as time unfolds; and in the long run, our
management will thereby become even more effective.

Typically, options about where to fund additional research projects
are determined based on subjective assessments. Using more rigorous decision
analysis, the benefit of acquiring missing or replacing poor information
can be simulated, thus determining how it might change the uncertainty
associated with particular management actions. Using such analytical logic,
the acquisition of new data and knowledge becomes an objective rather than
subjective process. It avoids keying in on simply what appears to be the
weakest link in the knowledge chain, and instead identifies the link that
will increase the adequacy of management decisions the most. This is often
defined as maximizing the probability of achieving objectives while minimizing
the risk of an undesirable outcome.

Benefits of New Approaches

Innovative approaches are critical because the exhaustive evaluation
of every combination of alternatives, and tracking the associated logic,
may be impractical even with current computer technology. Modern procedures
and search algorithms can reduce dimensionality, structure knowledge efficiently,
and use probabilistic strategies to find completely satisfactory solutions,
recognizing that "the best" solution may be unrealistic to ascertain.
Where the number of potential decisions and associated outcomes is very
large, decision support might be thought of as simply optimizing the course
of action. In reality, however, the situation is more akin to that of winnowing
the nearly incalculable number of possibilities to several, and subsequently
suggesting one. And, potential courses of action can be evaluated on the
risks associated with failure as well as probabilities of success. The
usefulness of the recommendation is enhanced by including an explanation
of the logical process of how the decision was reached.

Several of the direct benefits of approaching resource management and research
from this perspective accrue from an enhanced ability to analyze and compare
management scenarios. Through this analysis of alternative plans, the probability
of short term catastrophe can be minimized, and the opportunities for long
term success can be maximized. An added benefit of this type of framework
is that justification of decisions becomes more straightforward and quantitative.
Adaptive management integrates the setting of quantitative objectives,
exploration of alternative management strategies, monitoring of progress,
and evaluation of performance in terms of risks and benefits. Managers
must still make the actual decisions on the ground, but decision support
systems allow them to do so with greater confidence that the decisions
are based on all the currently available knowledge, and that the decisions
take correct account of the consequences of uncertainty.

When Should Adaptive Management Be Considered?

Adaptive management is most effective when situations have the following
characteristics:

The stakes are high: mistakes can be expensive, but rewards from determining
optimum management would also be high.

There is consensus about management goals, and how to recognize success.
It must also be possible to objectively monitor progress towards reaching
objectives.

A spectrum of plausible management choices exists, and there is strong
probability that at least some will work.

Uncertainty in the system is considerable, complicating any attempt
to make clear management choices by informal methods. And, there is reasonable
promise that uncertainty would significantly be reduced (i.e., major decisions
would be more straightforward) with well designed experiments.

Four capabilities are provided in effectively applying adaptive management
and decision support in complex ecological systems.

First, a computerized framework will represent vast amounts of knowledge
and data.

Second, the uncertainty associated with this knowledge and data is delineated
and used to benefit decision making.

Third, there must be the ability to examine a very large number of alternative,
potential management scenarios.

Last, complex resource management systems along with their logic and
the degrees of scientific confidence associated with recommendations must
be visualized and communicated to the actual decision makers.

These four capabilities can be handled in many ways. Our emphasis is
on Bayesian analysis, data visualization, and various artificial intelligence
techniques such as expert systems.